Cross-lingual summarization consists of generating a summary in one language given an input document in a different language, allowing for the dissemination of relevant content across speakers of other languages. However, this task remains challenging, mainly because of the need for cross-lingual datasets and the compounded difficulty of summarizing and translating. This work presents $\mu$PLAN, an approach to cross-lingual summarization that uses an intermediate planning step as a cross-lingual bridge. We formulate the plan as a sequence of entities that captures the conceptualization of the summary, i.e. identifying the salient content and expressing in which order to present the information, separate from the surface form. Using a multilingual knowledge base, we align the entities to their canonical designation across languages. $\mu$PLAN models first learn to generate the plan and then continue generating the summary conditioned on the plan and the input. We evaluate our methodology on the XWikis dataset on cross-lingual pairs across four languages and demonstrate that this planning objective achieves state-of-the-art performance in terms of ROUGE and faithfulness scores. Moreover, this planning approach improves the zero-shot transfer to new cross-lingual language pairs compared to non-planning baselines.

翻译：跨语言摘要旨在将一种语言的输入文档生成为另一种语言的摘要，从而促进相关内容在不同语言使用者间的传播。然而，由于需要跨语言数据集以及摘要生成与翻译任务的叠加难度，该任务仍具挑战性。本文提出$μ$PLAN方法，通过引入中间规划步骤作为跨语言桥梁来实现跨语言摘要。我们将规划定义为捕捉摘要概念化过程的实体序列（即识别显著内容并确定信息呈现顺序），使其独立于表层形式。利用多语言知识库，我们将实体与其在不同语言中的规范称谓对齐。$μ$PLAN模型首先学习生成规划，随后基于规划与输入继续生成摘要。我们在XWikis数据集上针对四种语言构成的跨语言对评估该方法，结果表明该规划目标在ROUGE分数与忠实度指标上均达到最优性能。此外，与无规划基线相比，该规划方法提升了向新跨语言对进行零样本迁移的能力。